5_PLSC_grad_GSR_combat_(SSD)
Ju-Chi.Yu
6/6/2022
Grabbing SPINS gradients
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.6 v dplyr 1.0.7
## v tidyr 1.1.4 v stringr 1.4.0
## v readr 2.0.1 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()## Warning: package 'ggseg' was built under R version 4.1.3## Loading required package: prettyGraphs## Loading required package: ExPosition##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
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## layout##
## Attaching package: 'colorspace'## The following object is masked from 'package:PTCA4CATA':
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## lightenRead in the SPINS big table
## New names:
## * `` -> ...1## Rows: 164640 Columns: 8## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (4): ROI, Network, Subject, Site
## dbl (4): ...1, grad1, grad2, grad3##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.read subject data
## [1] "record_id" "scanner"
## [3] "diagnostic_group" "demo_sex"
## [5] "demo_age_study_entry" "scog_rmet_total"
## [7] "scog_er40_total" "scog_tasit1_total"
## [9] "scog_tasit2_sinc" "scog_tasit2_simpsar"
## [11] "scog_tasit2_parsar" "scog_tasit3_lie"
## [13] "scog_tasit3_sar" "np_domain_tscore_process_speed"
## [15] "np_domain_tscore_att_vigilance" "np_domain_tscore_work_mem"
## [17] "np_domain_tscore_verbal_learning" "np_domain_tscore_visual_learning"
## [19] "np_domain_tscore_reasoning_ps"## New names:
## * `` -> ...1## Rows: 467 Columns: 43## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (4): record_id, scanner, diagnostic_group, demo_sex
## dbl (36): ...1, demo_age_study_entry, scog_rmet_total, scog_er40_total, scog...
## lgl (3): exclude_MRI, exclude_meanFD, exclude_earlyTerm##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.## [1] "scog_rmet_total" "scog_er40_total"
## [3] "scog_tasit1_total" "scog_tasit2_parsar"
## [5] "scog_tasit2_simpsar" "scog_tasit2_sinc"
## [7] "scog_tasit3_lie" "scog_tasit3_sar"
## [9] "np_domain_tscore_att_vigilance" "np_domain_tscore_process_speed"
## [11] "np_domain_tscore_work_mem" "np_domain_tscore_verbal_learning"
## [13] "np_domain_tscore_visual_learning" "np_domain_tscore_reasoning_ps"
## [15] "fd_mean_rest" "bsfs_sec2_total"
## [17] "bsfs_sec3_total" "bsfs_sec4_total"
## [19] "bsfs_sec5_total" "bsfs_sec6_total"
## [21] "qls20_empathy" "qls_factor_interpersonal"
## [23] "qls_factor_instrumental_role" "qls_factor_intrapsychic"
## [25] "qls_factor_comm_obj_activities" "bprs_factor_neg_symp"
## [27] "bprs_factor_pos_symp" "bprs_factor_anxiety_depression"
## [29] "bprs_factor_activation" "bprs_factor_hostility"Check subject overlap
grad.sub <- spins_grads_wide$Subject[order(spins_grads_wide$Subject)]
behav.sub <- lol_spins_behav_ssd$record_id[order(lol_spins_behav_ssd$record_id)]
# behav.sub[behav.sub %in% grad.sub == FALSE]
# grad.sub[grad.sub %in% behav.sub == FALSE]
complete.cases(spins_grads_wide)## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
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## [241] TRUE TRUE TRUE TRUE TRUE TRUEkept.sub <- lol_spins_behav_ssd$record_id[complete.cases(lol_spins_behav_ssd)==TRUE] # 246
## grab the matching data
behav.dat <- lol_spins_behav_ssd[kept.sub,c(6:19, 22:37)]
spins_grads_wide_org <- spins_grads_wide[,-1]
rownames(spins_grads_wide_org) <- spins_grads_wide$Subject
grad.dat <- spins_grads_wide_org[kept.sub,]
## variables to regress out
regout.dat <- var2regout_num[kept.sub,]Demographics
# lol_demo <-
# read_csv('../data/spins_lolivers_subject_info_for_grads_2022-04-21(withcomposite).csv') %>%
# filter(exclude_MRI==FALSE,
# exclude_meanFD==FALSE,
# exclude_earlyTerm==FALSE) %>% as.data.frame
# lol_demo$subject <- sub("SPN01_", "sub-", lol_demo$record_id) %>% sub("_", "", .)
# rownames(lol_demo) <- lol_demo$record_id
# lol_demo_match <- lol_demo[kept.sub,]
#
# spins_demo <- lol_demo_match %>%
# select(demo_sex, demo_age_study_entry, diagnostic_group, scog_rmet_total, scog_er40_total, #scog_mean_ea,
# scog_tasit1_total,
# scog_tasit2_total, scog_tasit3_total,np_composite_tscore, np_domain_tscore_att_vigilance,
# np_domain_tscore_process_speed, np_domain_tscore_work_mem,
# np_domain_tscore_verbal_learning, np_domain_tscore_visual_learning,
# np_domain_tscore_reasoning_ps,
# #bsfs_sec2_total, bsfs_sec3_total, bsfs_sec3_total, bsfs_sec4_total, bsfs_sec5_total, bsfs_sec6_total,
# #fd_mean_rest
# ) %>% data.frame
# colnames(spins_demo)
# rownames(spins_demo) <- lol_demo_match$subject
sub.dx <- spins_dx_org[kept.sub,]
sub.dx %>%
group_by(diagnostic_group) %>%
summarise_if(is.numeric, mean, na.rm = TRUE) %>% t## [,1]
## diagnostic_group "case"
## demo_age_study_entry "31.35772"sub.dx %>%
group_by(diagnostic_group) %>%
summarize_if(is.numeric, sd, na.rm = TRUE) %>% t## [,1]
## diagnostic_group "case"
## demo_age_study_entry "9.774294"cbind(table(sub.dx$diagnostic_group, sub.dx$demo_sex), table(sub.dx$diagnostic_group))## female male
## case 79 167 246Regress out the effects
table(regout.dat$demo_sex_num)##
## 0 1
## 79 167behav.reg <- apply(behav.dat, 2, function(x) lm(x~regout.dat$demo_sex + regout.dat$demo_age_study_entry + regout.dat$fd_mean_rest)$residual)
grad.reg <- apply(grad.dat, 2, function(x) lm(x~regout.dat$demo_sex + regout.dat$demo_age_study_entry + regout.dat$fd_mean_rest)$residual)
grad.reg2plot <- apply(grad.dat, 2, function(x){
model <- lm(x~regout.dat$demo_sex + regout.dat$demo_age_study_entry + regout.dat$fd_mean_rest)
return(model$residual + model$coefficient[1])
} )grab some network colours
networks <- read_delim("../networks.txt",
"\t", escape_double = FALSE, trim_ws = TRUE) %>%
select(NETWORK, NETWORKKEY, RED, GREEN, BLUE, ALPHA) %>%
distinct() %>%
add_row(NETWORK = "Subcortical", NETWORKKEY = 13, RED = 0, GREEN=0, BLUE=0, ALPHA=255) %>%
mutate(hex = rgb(RED, GREEN, BLUE, maxColorValue = 255)) %>%
arrange(NETWORKKEY)## Rows: 718 Columns: 12## -- Column specification --------------------------------------------------------
## Delimiter: "\t"
## chr (4): LABEL, HEMISPHERE, NETWORK, GLASSERLABELNAME
## dbl (8): INDEX, KEYVALUE, RED, GREEN, BLUE, ALPHA, NETWORKKEY, NETWORKSORTED...##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.networks$hex <- darken(networks$hex, 0.2)
# oi <- networks$hex
# swatchplot(
# "-40%" = lighten(oi, 0.4),
# "-20%" = lighten(oi, 0.2),
# " 0%" = oi,
# " 20%" = darken(oi, 0.2),
# " 25%" = darken(oi, 0.25),
# " 30%" = darken(oi, 0.3),
# " 35%" = darken(oi, 0.35),
# off = c(0, 0)
# )
networks## # A tibble: 13 x 7
## NETWORK NETWORKKEY RED GREEN BLUE ALPHA hex
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 Visual1 1 0 0 255 255 #0707CF
## 2 Visual2 2 100 0 255 255 #5001D0
## 3 Somatomotor 3 0 255 255 255 #11C7C7
## 4 Cingulo-Opercular 4 153 0 153 255 #7D007D
## 5 Dorsal-Attention 5 0 255 0 255 #10C710
## 6 Language 6 0 155 155 255 #097A7A
## 7 Frontoparietal 7 255 255 0 255 #C7C70B
## 8 Auditory 8 250 62 251 255 #D105D2
## 9 Default 9 255 0 0 255 #CC0303
## 10 Posterior-Multimodal 10 177 89 40 255 #88492D
## 11 Ventral-Multimodal 11 255 157 0 255 #C97B05
## 12 Orbito-Affective 12 65 125 0 168 #336400
## 13 Subcortical 13 0 0 0 255 #000000get row and column designs
## match ROIs to networks
ROI.network.match <- cbind(spins_grads$ROI, spins_grads$Network) %>% unique
ROI.idx <- ROI.network.match[,2]
names(ROI.idx) <- ROI.network.match[,1]
### match networks with colors
net.col.idx <- networks$hex
names(net.col.idx) <- networks$NETWORK
## design matrix for subjects
diagnostic.col <- sub.dx$diagnostic_group %>% as.matrix %>% makeNominalData() %>% createColorVectorsByDesign()
rownames(diagnostic.col$gc) <- sub(".","", rownames(diagnostic.col$gc))
## design matrix for columns - behavioral
behav.dx <- matrix(nrow = ncol(behav.dat), ncol = 1, dimnames = list(colnames(behav.dat), "type")) %>% as.data.frame
behav.col <- c("scog" = "#F28E2B",
"np" = "#59A14F",
"bsfs" = "#D37295",
"bprs" = "#E15759",
"qls" = "#B07AA1",
"qls20" = "#B07AA1",
"sans" = "#FF9888")
behav.dx$type <- sub("(^[^_]+).*", "\\1", colnames(behav.dat))
behav.dx$type.col <- recode(behav.dx$type, !!!behav.col)
## design matrix for columns - gradient
grad.dx <- matrix(nrow = ncol(grad.dat), ncol = 4, dimnames = list(colnames(grad.dat), c("gradient", "ROI", "network", "network.col"))) %>% as.data.frame
grad.dx$gradient <- sub("(^[^.]+).*", "\\1", colnames(grad.dat))
grad.dx$ROI <- sub("^[^.]+.(*)", "\\1", colnames(grad.dat))
grad.dx$network <- recode(grad.dx$ROI, !!!ROI.idx)
grad.dx$network.col <- recode(grad.dx$network, !!!net.col.idx)
## get different alpha for gradients
grad.col.idx <- c("grad1" = "grey30",
"grad2" = "grey60",
"grad3" = "grey90")
grad.dx$gradient.col <- recode(grad.dx$gradient, !!!grad.col.idx)
## for heatmap
col.heat <- colorRampPalette(c("red", "white", "blue"))(256)Run PLS-C
pls.res <- tepPLS(behav.reg, grad.reg, DESIGN = sub.dx$diagnostic_group, make_design_nominal = TRUE, graphs = FALSE)## [1] "DESIGN has too many columns or not enough elements. If the current DESIGN fails, a default will be created."
## [1] "DESIGN is not dummy-coded matrix. Creating default."pls.boot <- data4PCCAR::Boot4PLSC(behav.reg, grad.reg, scale1 = TRUE, scale2 = TRUE, nIter = 1000, nf2keep = 4)## Registered S3 method overwritten by 'data4PCCAR':
## method from
## print.str_colorsOfMusic PTCA4CATA## Warning in matrix(svd.S$d, nJ, nf2keep, byrow = TRUE): data length [30] is not a
## sub-multiple or multiple of the number of rows [1176]## swith direction for dimension 3
pls.res$TExPosition.Data$fi[,1] <- pls.res$TExPosition.Data$fi[,1]*-1
pls.res$TExPosition.Data$fj[,1] <- pls.res$TExPosition.Data$fj[,1]*-1
pls.res$TExPosition.Data$pdq$p[,1] <- pls.res$TExPosition.Data$pdq$p[,1]*-1
pls.res$TExPosition.Data$pdq$q[,1] <- pls.res$TExPosition.Data$pdq$q[,1]*-1
pls.res$TExPosition.Data$lx[,1] <- pls.res$TExPosition.Data$lx[,1]*-1
pls.res$TExPosition.Data$ly[,1] <- pls.res$TExPosition.Data$ly[,1]*-1
## Scree plot
PlotScree(pls.res$TExPosition.Data$eigs)
## Print singular values
pls.res$TExPosition.Data$pdq$Dv## [1] 7.4957924 4.5198669 3.3296823 2.9688109 2.7442054 2.3820598 2.2777871
## [8] 2.1865953 2.0715893 2.0507293 2.0123651 1.8997406 1.7823803 1.7219805
## [15] 1.6886212 1.6450566 1.5729338 1.5238833 1.4643175 1.3527436 1.3274168
## [22] 1.2650906 1.2223427 1.1588112 1.1079301 1.0246014 0.9974894 0.9140810
## [29] 0.8251913 0.7856681## Print eigenvalues
pls.res$TExPosition.Data$eigs## [1] 56.1869037 20.4291972 11.0867845 8.8138383 7.5306633 5.6742088
## [7] 5.1883141 4.7811992 4.2914821 4.2054906 4.0496131 3.6090145
## [13] 3.1768794 2.9652167 2.8514416 2.7062113 2.4741207 2.3222204
## [19] 2.1442258 1.8299152 1.7620353 1.6004543 1.4941218 1.3428435
## [25] 1.2275092 1.0498080 0.9949852 0.8355440 0.6809407 0.6172744pls.res$TExPosition.Data$t## [1] 33.4600295 12.1658518 6.6023239 5.2487550 4.4846076 3.3790649
## [7] 3.0897083 2.8472661 2.5556332 2.5044241 2.4115971 2.1492149
## [13] 1.8918729 1.7658250 1.6980704 1.6115839 1.4733709 1.3829124
## [19] 1.2769143 1.0897383 1.0493149 0.9530913 0.8897689 0.7996807
## [25] 0.7309976 0.6251743 0.5925266 0.4975773 0.4055090 0.3675949## Compare the inertia to the largest possible inertia
sum(cor(behav.dat, grad.dat)^2)## [1] 181.9057sum(cor(behav.dat, grad.dat)^2)/(ncol(behav.dat)*ncol(grad.dat))## [1] 0.005156057Here, we show that the effect that PLSC decomposes is pretty small to begin with. The effect size of the correlation between the two tables is 92.40 which accounts for 0.0065 of the largest possible effect.
Results
Dimension 1
lxly.out[[1]]
gridExtra::grid.arrange(bar.grad1, bar.grad2, bar.grad3, ncol = 1)
PrettyBarPlot2(pls.res$TExPosition.Data$fi[,1],
threshold = 0,
color4bar = ifelse(pls.boot$bootRatiosSignificant.i[,1] == TRUE, behav.dx$type.col, "grey90"),
horizontal = FALSE, main = "Scores - behavioural")
cor.heat <- pls.res$TExPosition.Data$X %>% heatmap(col = col.heat)
## control
grad.dat.ctrl <- grad.dat[sub.dx$diagnostic_group == "control",]
behav.dat.ctrl <- behav.dat[sub.dx$diagnostic_group == "control",]
corX.ctrl <- cor(as.matrix(behav.dat.ctrl),as.matrix(grad.dat.ctrl))
heatmap(corX.ctrl[cor.heat$rowInd, cor.heat$colInd], col = col.heat, Rowv = NA, Colv = NA)## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## case
grad.dat.case <- grad.dat[sub.dx$diagnostic_group == "case",]
behav.dat.case <- behav.dat[sub.dx$diagnostic_group == "case",]
corX.case <- cor(as.matrix(behav.dat.case),as.matrix(grad.dat.case))
heatmap(corX.case[cor.heat$rowInd, cor.heat$colInd], col = col.heat, Rowv = NA, Colv = NA)
Dimension 2
lxly.out[[2]]
gridExtra::grid.arrange(bar.grad1, bar.grad2, bar.grad3, ncol = 1)
PrettyBarPlot2(pls.res$TExPosition.Data$fi[,2],
threshold = 0, color4bar = ifelse(pls.boot$bootRatiosSignificant.i[,2] == TRUE, behav.dx$type.col, "grey90"),
horizontal = FALSE, main = "Scores - behavioural")
dim1.est <- pls.res$TExPosition.Data$pdq$Dv[1]*as.matrix(pls.res$TExPosition.Data$pdq$p[,1], ncol = 1) %*% t(as.matrix(pls.res$TExPosition.Data$pdq$q[,1], ncol = 1))
cor.heat.res1 <- (pls.res$TExPosition.Data$X - dim1.est) %>% heatmap(col = col.heat)
Dimension 3
lxly.out[[3]]
gridExtra::grid.arrange(bar.grad1, bar.grad2, bar.grad3, ncol = 1)
PrettyBarPlot2(pls.res$TExPosition.Data$fi[,3],
threshold = 0, color4bar = ifelse(pls.boot$bootRatiosSignificant.i[,3] == TRUE, behav.dx$type.col, "grey90"),
horizontal = FALSE, main = "Scores - behavioural")
dim2.est <- (as.matrix(pls.res$TExPosition.Data$pdq$p[,1:2]) %*% pls.res$TExPosition.Data$pdq$Dd[1:2,1:2] %*% t(as.matrix(pls.res$TExPosition.Data$pdq$q[,1:2])))
cor.heat.res2 <- heatmap(pls.res$TExPosition.Data$X - dim2.est, col = col.heat)
Dimension 4
lxly.out[[4]]
gridExtra::grid.arrange(bar.grad1, bar.grad2, bar.grad3, ncol = 1)
PrettyBarPlot2(pls.res$TExPosition.Data$fi[,4],
threshold = 0,
color4bar = ifelse(pls.boot$bootRatiosSignificant.i[,4] == TRUE, behav.dx$type.col, "grey90"),
horizontal = FALSE, main = "Scores - behavioural")
dim3.est <- (as.matrix(pls.res$TExPosition.Data$pdq$p[,1:3]) %*% pls.res$TExPosition.Data$pdq$Dd[1:3,1:3] %*% t(as.matrix(pls.res$TExPosition.Data$pdq$q[,1:3])))
cor.heat.res3 <- heatmap(pls.res$TExPosition.Data$X - dim3.est, col = col.heat)
Back into the brain
Dimension 1
## merging atlas and data by 'label'
## merging atlas and data by 'label'
Dimension 2
## merging atlas and data by 'label'
## merging atlas and data by 'label'
Dimension 3
## merging atlas and data by 'label'
## merging atlas and data by 'label'
Dimension 4
## merging atlas and data by 'label'
## merging atlas and data by 'label'
Fancy figures
3D plot of the gradients
Dimension 1
We need to interpret the arrows with cautious, because only the direction and the magnitude are meaningful but not the end point.
Dimension 2
We need to interpret the arrows with cautious, because only the direction and the magnitude are meaningful but not the end point.
Dimension 3
We need to interpret the arrows with cautious, because only the direction and the magnitude are meaningful but not the end point.
